Monotonic type-2 fuzzy neural network and its application to thermal comfort prediction
Monotonic type-2 fuzzy neural network and its application to thermal comfort prediction
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DOI：
10.1007/s00521-012-1140-x
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发表时间：
2012-09
影响因子：
6
通讯作者：
Chengdong Li;J. Yi;Ming Wang;Guiqing Zhang
中科院分区：
文献类型：
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作者：
Chengdong Li;J. Yi;Ming Wang;Guiqing Zhang
本文研究了单调二型模糊神经网络（T2FNN），该网络可用于许多要求输入输出具有单调性的辨识和预测问题。首先给出了T2FNN参数的充分条件，以保证输入和输出之间的单调性。然后，建立了单调T2FNN的数据驱动设计模型。在单调性约束下，提出了一种混合算法来优化单调T2FNN的参数。该混合算法利用约束最小二乘法和基于惩罚函数的梯度下降算法实现合理的参数初始化和优化。最后，将单调T2FNN应用于热舒适性指标预测，验证了单调T2FNN的有效性。并与其它方法进行了比较。
This paper studies the monotonic type-2 fuzzy neural network (T2FNN), which can be adopted in many identification and prediction problems where the monotonicity property between the inputs and outputs is required. Sufficient conditions on the parameters of the T2FNN are first presented to ensure the monotonicity between the inputs and outputs. Then, data-driven design model for the monotonic T2FNN is built. Also, under the monotonicity constraints, a hybrid algorithm is provided to optimize the parameters of the monotonic T2FNN. This hybrid algorithm utilizes the constrained least squares method and the penalty function-based gradient descent algorithm to realize reasonable parameter initialization and optimization. At last, an application to the thermal comfort index prediction is given to verify the effectiveness of the monotonic T2FNN. Comparisons with other methods are also made.